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Keynote   Speakers

 

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Prof. P. N Suganthan

Nanyang Technological University

Title: Randomization Based Deep and Shallow Learning Methods for Classification

Keynote Day: 19 December 2020

Abstract:  This talk will first introduce the origins of randomization-based feedforward neural networks such as the popular instantiation called random vector functional link neural network (RVFL) originated in early 1990s. Subsequently, a performance comparison among the randomization-based feedforward neural network models will be presented. The talk will also include ensemble/deep randomization-based neural networks. Another randomization-based paradigm is the random forest which exhibits highly competitive performances. The talk will briefly describe heterogeneous oblique random forest. Kernel ridge regression will be also briefly introduced. The talk will also present extensive benchmarking studies using classification datasets.

Bio: Ponnuthurai Nagaratnam Suganthan received the B. A degree,  Postgraduate Certificate and M. A degree in Electrical and Information  Engineering from the University of Cambridge, UK in 1990, 1992 and 1994, respectively.After completing his PhD research in 1995, he served as a  pre-doctoral Research Assistant in the Dept. of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Dept. of Computer  Science and Electrical Engineering, University of Queensland in 1996–99. He  moved to Singapore in 1999. He was an Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013-2018) and an associate editor of the IEEE   Trans on Cybernetics (2012-2018). He is an associate editor of Applied Soft Computing (Elsevier, 2018-), Neurocomputing (Elsevier, 2018-), IEEE Trans on  Evolutionary Computation (2005-), Information Sciences (Elsevier, 2009-), Pattern Recognition(Elsevier, 2001-) and Int. J. of Swarm Intelligence Research (2009-)  Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary  Computation (2010-), an SCI Indexed Elsevier Journal. His co-authored SaDE   paper (published in April 2009) won the "IEEE Trans. on Evolutionary  Computation outstanding paper award" in 2012. His former PhD student, Dr  Jane Jing Liang, won the IEEE CIS Outstanding PhD dissertation award, in   2014. IEEE CIS Singapore Chapter won the best chapter award in Singapore in  2014 for its achievements in 2013 under his leadership. His research   interests include swarm and evolutionary algorithms, pattern recognition,  forecasting, randomized neural networks, deep learning and applications of   swarm, evolutionary & machine learning algorithms. His publications have  been well cited (Googlescholar Citations:~39k). His SCI indexed publications  attracted over 1000 SCI citations in a calendar year since 2013. He was   selected as one of the highly cited researchers by Thomson Reuters every year  from 2015 to 2018 in computer science. He served as the General Chair of the IEEE   SSCI 2013. He is an IEEE CIS distinguished lecturer (DLP) in 2018-2020. He   has been a member of the IEEE (S'91, M'92, SM'00, Fellow’15) since 1991 and   an elected AdCommember of the IEEE Computational Intelligence Society (CIS)   in 2014-2016.

Address: S2-B2a-21,   EEE, NTU, Singapore, 639798  Tel: 65-67905404  Fax: 65-67933318



Prof. Yihong Gong

Xi’an Jiaotong University University

Title: Brain-Inspired Machine Learning Methods

Keynote Day: 19 December 2020

Abstract:  In this talk, I will present four novel methods that are inspired by human brain visual cognitive mechanisms. First, we propose the Min-Max objective function inspired by the manifold separation property of human visual cortex, which enforces the CNN model to learn features with minimized within-class distances and maximized between-class distances. Second, we propose the L-21 norm-based objective function inspired by properties of neurons in the V-4 layer of human ventral pathway. It enforces the sparse category selectivity on neurons in the output layer of a CNN model. Third, we propose the CNN structure that is inspired by the dual-pathway mechanism of the human visual system and is able to solve the “texture bias” problem of the existing CNN models. To solve the “catastropic forgetting” problem that occurs when fine-tuning a CNN model with new training samples, we propose the Anchor Loss objective function that requires the CNN model to keep the topological structure of the learned feature space during the fine-tuning phase. This work is inspired by the latest cognitive scientific research on human visual memory. We also developed several methods to automatically capture topological structure of a learned feature space.These proposed objective functions are independent of and can be applied to any CNN models. Comprehensive performance evaluations show remarkable performance improvements of the representative CNN models on the respective tasks without increasing their model complexities.

Bio: Yihong Gong is a distinguished professor, the dean of School of Software Engineering of Xi’an Jiaotong University, an IEEE Fellow, and a vice director of the National Engineering Laboratory for Visual Information Processing. His research interests include image/video content analysis and machine learning algorithms. He is among the first batch of researchers in the world initiating research studies on content-based image retrieval, sports video event detection, text/video content summarization, and image classification using the sparse coding image features. He has published more than 200 technical papers and two monographs. To date, his works have received more than 22,000 citations (Google h-index=64), with over 3,500 citations for his most cited paper.  In 2015, his ACM SIGIR 2003 paper titled “Document Clustering Based on Non-Negative Matrix Factorization” received “Test of Time Award” Honorable Mentions by the ACM SIGIR Executive Committee. Under his supervision, his teams have won numerous international/domestic competitions in image/video content recognitions.


Prof. Qi Wang

Northwestern Polytechnical University

Title: Crowd Counting Research towards Real World Application

Keynote Day: 19 December 2020

Abstract:  In recent years, due to the frequent occurrence of large-scale activities, crowd density estimation is becoming significant. With the continuous development of deep learning and computer vision, the performance of crowd counting methods has been greatly improved. This report will introduce the crowd counting research towards the real world application. It mainly includes three aspects: (1) utilize virtual data to build a large-scale annotated crowd counting data set, and improve the generalization ability of crowd counting models through supervised learning and domain adaptation; (2) propose an inter-domain feature isolation model to translate synthetic data into real data, and use Gaussian priors to improve the quality of the density map addressing the problem of inter-domain differences and the generation of fine crowd density maps; (3) establish a large crowd counting data set and a benchmark for researchers to evaluate the  algorithm performance,  which will promote the rapid development of the crowd counting.

Bio: Qi Wang received the B.E. degree in automation and the Ph.D. degree in pattern recognition and intelligent systems from the University of Science and Technology of China, Hefei, China, in 2005 and 2010, respectively. He is currently a Professor with the School of Computer Science and with the Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, China. His research interests include computer vision and pattern recognition. He is the Section Editor-in-Chief of Remote Sensing, and the associate editors of IEEE T-CSVT, IEEE T-SMC:System, IEEE GRSL, etc.



 

Prof.   Shigang Yue

University of Lincoln

Title: Dealing with motion in the dynamic visual world – from insects   to neuromorphic sensors

Keynote Day:19 December 2020

Abstract: Animals, as small as insects, have amazing ability in coping  with the dynamic visual world. This ability has not been replicated so far to  human-made intelligent moving machines such as robots or intelligent  vehicles. In the last decades, different types of visual neurons in insects  have been identified with surprising preferences tuned to specific visual   cues. By modelling bio-plausible visual neurons and their pre-synaptic  networks, we can not only further our understanding of how animals visual  systems work, but also step forward in developing new vision systems for  future robots and vehicles. In this talk, I will introduce the bio-inspired   motion sensitive neural models developed in my group, and also talk about   their applications in robotics and autonomous vehicles.

Bio: Shigang Yue is a Professor in the School of Computer Science , University of Lincoln, United Kingdom. He is the founding director of  Computational Intelligence Lab (CIL) and the deputy director of Lincoln  Centre for Autonomous Systems (L-CAS). He received his PhD degrees from  Beijing University of Technology (BJUT) in 1996, worked in BJUT as a Lecturer   (1996-1998) and an Associate Professor (1998-1999), also in City University   of Hong Kong (MEEM) as a Senior Research Assistant (1998-1999). He was an   Alexander von Humboldt Research Fellow (2000, 2001) working with Prof.   Henrich in the Faculty of Computer Science, University of Kaiserslautern, Germany.  Before joining the University of Lincoln as a Senior Lecturer (2007) and  promoted to Professor (2012), he held positions in the University of  Cambridge (2006-2007), University of Newcastle (2003-2006) and the University  College London (UCL) (2002-2003) respectively.
  His research interests are mainly within the field of bio-inspired artificial   intelligence, computer vision, robotics, brains, and neuroscience. He is   particularly interested in biological plausible visual neural systems and its   applications in vehicles, interactive systems, UAVs and ground robots. He has  published about 200 papers in academic journals and conferences, many of them   are in top tier journals. He has chaired several international conferences   and has sit in the editorial board of several international journals.

Prof. Shigang   Yue home pages and short cv:

http://webpages.lincoln.ac.uk/syue

http://www.ciluk.org/syue

https://staff.lincoln.ac.uk/syue